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Robot localization using soft object detection


Anati, Roy; Scaramuzza, Davide; Derpanis, Konstantinos G; Daniilidis, Kostas (2012). Robot localization using soft object detection. In: IEEE International Conference on Robotics and Automation, St. Paul, USA, 14 May 2012 - 18 May 2012.

Abstract

In this paper, we give a new double twist to the robot localization problem. We solve the problem for the case of prior maps which are semantically annotated perhaps even sketched by hand. Data association is achieved not through the detection of visual features but the detection of object classes used in the annotation of the prior maps. To avoid the caveats of general object recognition, we propose a new representation of the query images that consists of a vector of the detection scores for each object class. Given such soft object detections we are able to create hypotheses about pose and to refine them through particle filtering. As opposed to small confined office and kitchen spaces, our experiment takes place in a large open urban rail station with multiple semantically ambiguous places. The success of our approach shows that our new representation is a robust way to exploit the plethora of existing prior maps for GPS-denied environments avoiding the data association problems when matching point clouds or visual features.

Abstract

In this paper, we give a new double twist to the robot localization problem. We solve the problem for the case of prior maps which are semantically annotated perhaps even sketched by hand. Data association is achieved not through the detection of visual features but the detection of object classes used in the annotation of the prior maps. To avoid the caveats of general object recognition, we propose a new representation of the query images that consists of a vector of the detection scores for each object class. Given such soft object detections we are able to create hypotheses about pose and to refine them through particle filtering. As opposed to small confined office and kitchen spaces, our experiment takes place in a large open urban rail station with multiple semantically ambiguous places. The success of our approach shows that our new representation is a robust way to exploit the plethora of existing prior maps for GPS-denied environments avoiding the data association problems when matching point clouds or visual features.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:03 Faculty of Economics > Department of Informatics
Dewey Decimal Classification:000 Computer science, knowledge & systems
Language:English
Event End Date:18 May 2012
Deposited On:24 Jan 2013 13:22
Last Modified:05 Aug 2017 20:21
Publisher:Institute of Electrical and Electronics Engineers
Series Name:IEEE International Conference on Robotics and Automation. Proceedings
ISSN:1050-4729
ISBN:978-1-4673-1404-6
Additional Information:© 2012 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Publisher DOI:https://doi.org/10.1109/ICRA.2012.6225216
Related URLs:http://www.icra2012.org/
Other Identification Number:merlin-id:7905

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